code: def get_config():
      parser = argparse.ArgumentParser()
  
      ## model_arg
      model_group = parser.add_argument_group("model_base_arg")
      model_group.add_argument("--num_layers", default=7, type=int)
      model_group.add_argument("--hidden_size", default=64, type=int)
      model_group.add_argument("--num_heads", default=4, type=int)
      model_group.add_argument("--dropout", default=0.1, type=float)
      model_group.add_argument("--attn_dropout", default=0, type=float)
      model_group.add_argument("--train_partition", default=9, type=int)
      model_group.add_argument("--eval_partition", default=3, type=int)
      model_group.add_argument("--cross_edge_feat", default=3, type=int)
      model_group.add_argument(
          "--cross_edge_feat_max_neigh", default=64, type=int
      )
  
      ## label_embed_arg
      embed_group = parser.add_argument_group("embed_arg")
      embed_group.add_argument("--use_label_e", action="store_true")
      embed_group.add_argument("--label_rate", default=0.5, type=float)
  
      ## train_arg
      train_group = parser.add_argument_group("train_arg")
      train_group.add_argument("--runs", default=10, type=int)
      train_group.add_argument("--epochs", default=5000, type=int)
      train_group.add_argument("--lr", default=0.001, type=float)
      train_group.add_argument("--place", default=-1, type=int)
      train_group.add_argument(
          "--log_file", default="result_proteins.txt", type=str
      )
      return parser.parse_args()
  
imports: import math
  import math
  import ssl
  from sklearn.metrics import roc_auc_score
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  import time
  import copy
  from ogb.nodeproppred import Evaluator
  import pickle
  from model import Proteins_baseline_model, Proteins_label_embedding_model
  from partition import RandomPartition
  from cross_edge_feat import cross_edge_feat
  from dataset.random_partition_dataset import RandomPartition
  import os
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: get_config() and returns: parser